Support is requested for a multidisciplinary predoctoral training program in Quantitative Biomedical Sciences (QBS) at Dartmouth College. The QBS program was founded in 2010 to provide cross-training in quantitative, computational and biomedical sciences to prepare students for a new research landscape that is increasingly characterized by complex problems, big data and multidisciplinary teams. The QBS program spans the entire Dartmouth College campus including faculty from the Schools of Arts and Sciences, Business, Engineering and Medicine. QBS admits 6-8 students per year from different backgrounds including computer science, mathematics, biology, chemistry, and engineering. These students are selected from a highly qualified national and international pool of candidates that are rich in both ethnic and racial diversity. As part of the proposed T32 training program, we will select two highly qualified predoctoral students each year for two years of support based on academic qualifications and a dissertation project that will focus on big data. The predoctoral trainees will complete an eight course core curriculum that will include two courses in computer science or data science, two courses in bioinformatics, two courses in biostatistics, two courses in epidemiology and a two course sequence in integrative biomedical sciences that exposes students to interdisciplinary research and that culminates in a collaborative big data class project. Students in this program take two elective courses, one term of teaching, journal clubs and seminars, training in responsible conduct of research, a written and oral qualifier exam, a yearly research in progress seminar, and the completion of a significant research project that forms the foundation of the written dissertation that is orally defended. Faculty trainers include QBS and computer science faculty that have extramural funding, a strong track record of graduate training experience, a strong track record of big data research and a commitment to participating in the activities necessary for successful predoctoral training. Research areas of the faculty include bioinformatics, biostatistics, computer science, computational biology, genomics, and epidemiology. It is our vision that the next generation of big data researchers need multiple skills and expertise from areas such as bioinformatics, biostatistics, computer science and epidemiology to bring together teams of researchers.